DatriseAI-first ETL

CoinAPI to Amazon S3 Data Lake Integration

AI-first ETL from CoinAPI into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.

How Datrise loads CoinAPI into Amazon S3 Data Lake

Datrise syncs CoinAPI's exchange rates, symbols, trades, order book snapshots, OHLCV bars, and exchanges into Amazon S3 Data Lake as columnar Parquet objects partitioned per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.

Sync is incremental: Datrise uses writes new date partitions and compacts small files on a schedule, so re-runs update only what changed. Hive-style path partitioning (entity/date) for engine-agnostic reads. A lake has no schema enforcement, so Datrise writes a schema manifest alongside the data to keep downstream engines consistent.

Ideal for an open, engine-neutral storage layer for Spark, Athena, Trino, or DuckDB.

Endpoints

CoinAPI: Consolidated crypto market data API.

Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.

How CoinAPI entities map to Amazon S3 Data Lake

CoinAPI entityAmazon S3 Data Lake objectNotes
exchange ratescoin_api_exchange_ratesISO-8601 timestamp columns events
symbolscoin_api_symbolsid PK · linked to coin_api_exchange_rates
tradescoin_api_tradesid PK · linked to coin_api_exchange_rates
order book snapshotscoin_api_order_book_snapshotsid PK · linked to coin_api_exchange_rates

FAQ

How does Datrise handle CoinAPI's custom fields in Amazon S3 Data Lake?

Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon S3 Data Lake types.

How does the CoinAPI to Amazon S3 Data Lake sync stay up to date?

It runs incrementally — Datrise uses writes new date partitions and compacts small files on a schedule.

Related pipelines

Early access

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